A Novel Approach to Sparse Inverse Covariance Estimation Using Transform Domain Updates and Exponentially Adaptive Thresholding

TL;DR

This paper introduces a new sparse inverse covariance estimation method that employs transform domain updates and adaptive thresholding, demonstrating improved accuracy over existing techniques.

Contribution

The paper presents a novel SICE algorithm using transform domain updates and exponential adaptive thresholding, along with convergence analysis.

Findings

Outperforms state-of-the-art methods in accuracy

Effective in recovering covariance graph structures

Provides convergence guarantees for the proposed algorithm

Abstract

Sparse Inverse Covariance Estimation (SICE) is useful in many practical data analyses. Recovering the connectivity, non-connectivity graph of covariates is classified amongst the most important data mining and learning problems. In this paper, we introduce a novel SICE approach using adaptive thresholding. Our method is based on updates in a transformed domain of the desired matrix and exponentially decaying adaptive thresholding in the main domain (Inverse Covariance matrix domain). In addition to the proposed algorithm, the convergence analysis is also provided. In the Numerical Experiments Section, we show that the proposed method outperforms state-of-the-art methods in terms of accuracy.